In recent years, utility-scale hybrid power plants (HPPs) have emerged as promising electricity generation resources by combining multiple generation technologies and storage capabilities. This paper presents a novel framework for optimizing the offering and operation of HPPs in the voluntary balancing market, specifically for providing regulating power as a balancing service. The proposed framework utilizes a two-level robust optimization approach, where the first level focuses on look-ahead offering and operation, and the second level handles real-time re-scheduling of generation. Uncertainties arising from wind power and regulating prices are considered as decision-independent uncertainties (DIU). Conversely, the decisions regarding regulating power offers influence the uncertainty associated with activated regulating volumes, leading to decision-dependent uncertainties (DDU). To tackle the model incorporating both DIU and DDU, this paper introduces a customized nested adaptive column and constraint generation (NAC&CG) algorithm that ensures global convergence. The case studies demonstrate the effectiveness of the proposed model in enabling HPPs to accurately track the activated regulating volumes, ensuring reliable provision of balancing service.

Since hybrid power plants (HPPs) play an intensive role in the energy supply balance of future energy systems, there is today an increased attention on co-located wind-battery HPPs both in industry and academia. However, the profitability of HPPs in sequential electricity markets to overcome initial investment costs has not been yet well examined, especially with respect to  balancing services provision. This article proposes a novel energy management system (EMS) for optimal participation of wind-battery HPPs in two sequential electricity markets, namely in spot market and balancing market. The methodology consists of three optimization models, which allow HPPs to achieve energy arbitrage, to provide balancing services, and to reduce real-time imbalance costs. Furthermore, the profitability of HPPs in future (2030) energy scenario is analyzed based on the new designed and developed EMS taking balancing service into account. The results of the overplanting case show that HPPs operated based on the proposed EMS achieve net present value 3.6 times as high as sole operation in spot market.

Utility-scale and small scale wind and solar power installations along with electric vehicle charging stations, and other active sources of energy are increasing at the medium and lower voltage levels in the distribution grid. This situation required a better understanding of the impact of high penetration of weather-dependent renewable energy sources on the operating conditions of the distribution network at both medium and low voltage levels. Despite the need, a multi-voltage level distribution network model, based on real network data and weather-dependent renewable generation data, has not been presented for distribution grid studies. This paper presents a comprehensive multi-voltage level active distribution network model based on real network data along with load and generation time-series for about a year. The network topology is modelled based on geographical data for various rural, semi-urban, and urban locations. The distribution network is embodied with a large share of renewable generation sources, with generation time-series simulated from meteorological data. The network is also flexible to incorporate other assets such as electric vehicle charging stations, storage, etc. Thus, the presented active distribution network model can be used to study, optimize, and control the effects of weather dependent generation and other network assets in the distribution grid.

The utility-scale co-located hybrid power plants (HPPs) have been receiving attention globally due to enhanced controllability and efficient utilization of electrical infrastructure. While power plant control has been extensively studied for single-technology power plants in the past decades, how to control a co-located HPP that includes sub-plants with multiple technologies is yet to be well defined. To fill the gap, this paper proposes a novel hierarchical control architecture for co-located HPPs. This control architecture contains four control levels: asset control level, plant control level, HPP control level and HPP energy management system (EMS) level. The objective of HPP EMS level is to find optimal strategies for market participation, and the objective of HPP control level is to execute those strategies from the HPP EMS in real time. The interactions across the control hierarchy are firstly discussed in this paper, where attention is closely paid to interactions between HPP EMS level and HPP control level, and between HPP control level and plant control level. Novel strategies for control coordination are presented to ensure all the control levels work together without counteracting against each other. Frequency control and fault ride-through are two examples to demonstrate such control coordination.

The increased penetration of wind power plants (WPPs) in distribution networks challenges the distribution system operators (DSOs) to improve and optimize networks’ operation. A higher amount of local power production translates to more losses in the network. This paper proposes a deterministic optimization methodology to minimize the losses in distribution networks with WPPs, by exploiting WPPs’ capability to control reactive power in coordination with the on-load tap changers from the MV/HV transformer, avoiding the need for network reinforcements. The principal objective is to optimize the reactive power flow in the network. Measurements from a real distribution network with a large share of controllable WPPs under varying wind and load conditions are used for the study. The benefits and the challenges of the optimization methodology are assessed and discussed with respect to active power losses, voltage profile and reactive power. The results show that with reactive power support from WPPs, network losses are reduced by 4.2 %. Higher loss reductions (up to 19 %) can be achieved through a coordinated action between the WPPs and TSO. Furthermore, it is shown that the distribution network can act as an asset to the transmission network for reactive power support, via actively controlling WPP’s reactive power.

Offshore grids can have an important role in the transition of the energy system to sustainability. Although they require extensive infrastructure investments, they open up for the exploitation of a vast amount of additional resources and may be important to provide for part of the increasing electricity demand driven by sector coupling between the electricity, heat, and transport sectors. We show that the level of such sector coupling is decisive for the development of offshore grids. Performing energy system optimization in a model application of the Northern-central European energy system and the North Sea offshore grid towards 2050, we find that without sector coupling no offshore grid may develop, and that the higher the level of sector coupling, the higher the value of offshore grids. The electrification of the transport sector favors offshore grid development more than the electrification of the heat sector. Offshore grid infrastructure development can therefore not be discussed as a separate political topic, but seen in connection to sector coupling.

Increasing share of renewables poses new challenges towards planning and operation of power systems all over the world, thereby, requiring new methods and tools. This article explains the value of balancing tool chain which encompasses operation of market and power system from day-ahead planning until the real-time dynamic simulation. The salient features for such a balancing tool chain is discussed in this article. A Balancing Tool Chain has been developed and validated based on balancing principles of Denmark and Europe. The efficacy of the balancing tool chain is demonstrated through very detailed case studies of massive penetration of offshore wind power in North Sea countries towards 2050. Using detailed case studies, recommendations are provided towards balancing of North Sea countries in terms of operational and balancing practices as well as dimensioning of reserves for future scenarios.

This paper proposes a mathematical model to perform optimal operational planning of large-scale energy systems with high share of renewable energy. Furthermore, it analyses the influence of different unit commitment modelling approaches on the operational planning outcomes. The value of co-optimisation of electricity and heating sector is emphasized in this paper. The results show the influence of massive renewable penetration in the energy sector towards 2050, and how this influences generation from other sources such as thermal and hydro. Including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. Neglecting the unit commitment constraints leads to inaccurate results in terms of underestimation of costs, curtailment, wind's and solar PV's average revenue per energy unit sold, price volatility, and to overestimation of the flexibility of thermal units. Hence, depending on the purpose of the analysis, it is recommended to consider carefully the choice of unit commitment modelling approach and acknowledge the limitations. When the focus is on prices and revenues, using unit commitment constraints with integer variables is preferable.

In order to participate in energy market, variable renewable energy sources need to reduce the uncertainty of forecast errors. Inclusion of storage can be a viable option not only to minimize the penalties due to forecast uncertainties but also to maximize the revenue generation. This paper presents a decision framework for respecting the market constraints and maximise the revenues of a wind and storage power plant. Wind power and price forecast are used in convex optimisation algorithm for making day ahead decisions on battery operation. This day ahead optimisation results feed to an algorithm for operating in the balancing market. Several scenarios and case studies have been simulated to assess the value of storage for revenue maximization of a wind power plant. The results show that proposed algorithms can increase the revenue by more than 10% compared to the operation of wind power plant without battery.